基于心电图的深度学习和临床风险因素来预测心房
Shaan Khurshid1,2,3, Samuel Friedman4, Christopher Reeder4
1Division of Cardiology (S.K., M.A.A., J.E.H.), Massachusetts General Hospital, Boston.
Circulation
|November 8, 2021
概括
对心电图的人工智能 (AI) 分析显示,对心房风险的预测能力与传统的临床评分相似. 将人工智能与临床因素 (CH-AI) 结合起来,可以预测未来的肌肉外表事件的最高准确度.
科学领域:
- 心脏病学
- 人工智能
- 医疗诊断
背景情况:
- 估计发生心房的风险对于预防性护理至关重要.
- 通过人工智能对12导心电图的分析显示了有效预测AF风险的潜力.
- 除了已确定的AF临床风险因素外,AI的附加价值仍在调查中.
研究的目的:
- 评估人工智能模型 (ECG-AI) 对于发生的AF风险的预测准确性.
- 将ECG- AI的性能与CHARGE- AF临床风险得分进行比较.
- 评估ECG-AI和CHARGE-AF对AF风险的综合预测能力.
主要方法:
- 一个卷积神经网络 (ECG-AI) 被训练来预测来自12个导向心电图的5年AF风险.
- 使用ECG-AI,CHARGE-AF和联合CH-AI方法进行了考克斯的比例危险模型.
- 在内部和外部验证队列中使用区分 (AUC) 和校准来评估模型的性能.
主要成果:
- 综合的CH-AI模型在所有测试组中显示出最高的预测精度 (AUC).
- 每个ECG-AI和CHARGE-AF的预测性能都是相似的.
- 在ECG-AI和CH-AI模型中观察到较低的校准误差,表明可靠的风险估计.
结论:
- 以人工智能为基础的心电图分析提供了与发生性肌痛风险得分相比较的预测效用.
- 电脑心电图AI模型和临床风险因素是互补的,提高了整体预测准确性.
- 通过ECG-AI,可以有效和准确地量化未来的AF风险.
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